import numpy as np
import pandas as pd
import seaborn as sns
import plotly.graph_objects as go
import matplotlib.pyplot as plt
import folium
df = pd.read_csv('case_time_series (2).csv')
pip install folium
Requirement already satisfied: folium in c:\users\roshi\anaconda3\lib\site-packages (0.14.0) Requirement already satisfied: branca>=0.6.0 in c:\users\roshi\anaconda3\lib\site-packages (from folium) (0.6.0) Requirement already satisfied: jinja2>=2.9 in c:\users\roshi\anaconda3\lib\site-packages (from folium) (2.11.3) Requirement already satisfied: requests in c:\users\roshi\anaconda3\lib\site-packages (from folium) (2.28.1) Requirement already satisfied: numpy in c:\users\roshi\anaconda3\lib\site-packages (from folium) (1.21.5) Requirement already satisfied: MarkupSafe>=0.23 in c:\users\roshi\anaconda3\lib\site-packages (from jinja2>=2.9->folium) (2.0.1) Requirement already satisfied: certifi>=2017.4.17 in c:\users\roshi\anaconda3\lib\site-packages (from requests->folium) (2022.9.14) Requirement already satisfied: charset-normalizer<3,>=2 in c:\users\roshi\anaconda3\lib\site-packages (from requests->folium) (2.0.4) Requirement already satisfied: urllib3<1.27,>=1.21.1 in c:\users\roshi\anaconda3\lib\site-packages (from requests->folium) (1.26.11) Requirement already satisfied: idna<4,>=2.5 in c:\users\roshi\anaconda3\lib\site-packages (from requests->folium) (3.3) Note: you may need to restart the kernel to use updated packages.
import folium
df
| Date | Daily Confirmed | Total Confirmed | Daily Recovered | Total Recovered | Daily Deceased | Total Deceased | |
|---|---|---|---|---|---|---|---|
| 0 | 30 January | 1 | 1 | 0 | 0 | 0 | 0 |
| 1 | 31 January | 0 | 1 | 0 | 0 | 0 | 0 |
| 2 | 01 February | 0 | 1 | 0 | 0 | 0 | 0 |
| 3 | 02 February | 1 | 2 | 0 | 0 | 0 | 0 |
| 4 | 03 February | 1 | 3 | 0 | 0 | 0 | 0 |
| ... | ... | ... | ... | ... | ... | ... | ... |
| 100 | 09 May | 3175 | 62865 | 1414 | 19301 | 115 | 2101 |
| 101 | 10 May | 4311 | 67176 | 1669 | 20970 | 112 | 2213 |
| 102 | 11 May | 3592 | 70768 | 1579 | 22549 | 81 | 2294 |
| 103 | 12 May | 3562 | 74330 | 1905 | 24454 | 120 | 2414 |
| 104 | 13 May | 3726 | 78056 | 1964 | 26418 | 136 | 2550 |
105 rows × 7 columns
sns.pairplot(df)
<seaborn.axisgrid.PairGrid at 0x2a26ae6c0a0>
import plotly.graph_objects as go
import pandas as pd
# reading the database
data = pd.read_csv('case_time_series (2).csv')
plot = go.Figure(data=[go.Scatter(
x=data['Date'],
y=data['Daily Confirmed'],
mode='markers',)
])
# Add dropdown
plot.update_layout(
updatemenus=[
dict(
type="buttons",
direction="left",
buttons=list([
dict(
args=["type", "bar"],
label="bar plot",
method="restyle"
),
dict(
args=["type", "line"],
label="lineplot",
method="restyle"
),
]),
),
]
)
plot.show()
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
data = pd.read_csv('case_time_series (2).csv')
Y = data.iloc[61:,1].values
R = data.iloc[61:,3].values
D = data.iloc[61:,5].values
X = data.iloc[61:,0]
plt.figure(figsize=(25,8))
ax = plt.axes()
ax.grid(linewidth=0.4, color='#8f8f8f')
ax.set_facecolor("black")
ax.set_xlabel('\nDate',size=25,color='#4bb4f2')
ax.set_ylabel('Number of Confirmed Cases\n',
size=25,color='#4bb4f2')
plt.xticks(rotation='vertical',size='20',color='red')
plt.yticks(size=20,color='red')
plt.tick_params(size=20,color='red')
for i,j in zip(X,Y):
ax.annotate(str(j),xy=(i,j+100),color='white',size='13')
ax.annotate('Second Lockdown 15th April',
xy=(15.2, 860),
xytext=(19.9,500),
color='white',
size='25',
arrowprops=dict(color='red',
linewidth=0.025))
plt.title("COVID-19 IN : Daily Confirmed\n",
size=50,color='#28a9ff') #28a9ff
ax.plot(X,Y,
color='#1F77B4',
marker='*',
linewidth=4,
markersize=15,
markeredgecolor='#28a9ff')
[<matplotlib.lines.Line2D at 0x2a2669cdca0>]
data = pd.read_csv('district (1).csv')
data.head()
re=data.iloc[:15,5].values
de=data.iloc[:15,4].values
co=data.iloc[:15,3].values
x=list(data.iloc[:15,0])
plt.figure(figsize=(13,10))
ax=plt.axes()
ax.set_facecolor('black')
ax.grid(linewidth=0.4, color='#8f8f8f')
plt.xticks(rotation='vertical',size='20',color='black')#ticks of X
plt.yticks(size='20',color='black')
ax.set_xlabel('\nDistrict',size=25,color='#4bb4f2') #17th may 2020
ax.invert_xaxis()
ax.set_ylabel('No. of cases\n',size=25,color='#4bb4f2')
plt.tick_params(size=20,color='white')
ax.set_title('andhrapradesh District wise breakdown\n',size=50,color='#28a9ff')
plt.bar(x,co,label='re')
plt.bar(x,re,label='re',color='green')
plt.bar(x,de,label='re',color='red')
for i,j in zip(x,co):
ax.annotate(str(int(j)),xy=(i,j+3),color='white',size='15')
plt.legend(['Confirmed','Recovered','Deceased'],
fontsize=20)
<matplotlib.legend.Legend at 0x2a271f4b280>
data
| districtData/0/district | blank | districtData/0/active | districtData/0/confirmed | districtData/0/deceased | districtData/0/recovered | |
|---|---|---|---|---|---|---|
| 0 | anthapur | NaN | 47 | 122 | 4 | 71 |
| 1 | chittor | NaN | 100 | 177 | 0 | 77 |
| 2 | eastgodavari | NaN | 13 | 52 | 0 | 39 |
| 3 | guntur | NaN | 134 | 417 | 8 | 275 |
| 4 | kadapa | NaN | 34 | 102 | 0 | 68 |
| 5 | krishna | NaN | 99 | 367 | 15 | 253 |
| 6 | kurnool | NaN | 187 | 611 | 19 | 405 |
| 7 | nellore | NaN | 53 | 150 | 3 | 94 |
| 8 | prakasam | NaN | 3 | 66 | 0 | 63 |
| 9 | srikakulam | NaN | 10 | 14 | 0 | 4 |
| 10 | vishakapatnam | NaN | 41 | 75 | 1 | 33 |
| 11 | vizayanagram | NaN | 7 | 7 | 0 | 0 |
| 12 | weatgodavari | NaN | 19 | 70 | 0 | 51 |
import plotly.express as px
import pandas as pd
DISTRICTS = ["westgodavari", "vizianagaram", "visakhapatnam", "srikakulam", "prakasam", "nellore",
"kurnool", "krishna", "kadapa", "guntur","east godavari","chittor","ananthapur"]
ACTIVE= ["47", "100", "13", "134", "34","99",
"187", "53", "3", "10", "41","7","19"]
CONFIRMED = ["122", "177", "52", "417", "102","367" ,"611" ,
"150", "66", "14", "75", "7","70"]
RECOVERED = ["4", "0", "0", "8", "0","15",
"19", "3", "0", "0", "1","1","1"]
df = pd.DataFrame(dict(DISTRICTS=DISTRICTS, ACTIVE=ACTIVE, CONFIRMED=CONFIRMED, RECOVERED=RECOVERED))
fig = px.sunburst(df, path=['DISTRICTS','ACTIVE','CONFIRMED','RECOVERED'] )
fig.show()
import pandas as pd
df= pd.read_csv('Covid cases in India (3).csv') #states of covid cases
df
| S. No. | Name of State / UT | Total Confirmed cases (Indian National) | Total Confirmed cases ( Foreign National ) | Cured | Death | |
|---|---|---|---|---|---|---|
| 0 | 1 | Andhra Pradesh | 12 | 0 | 1 | 0 |
| 1 | 2 | Chhattisgarh | 6 | 0 | 0 | 0 |
| 2 | 3 | Delhi | 38 | 1 | 6 | 1 |
| 3 | 4 | Gujarat | 43 | 0 | 0 | 3 |
| 4 | 5 | Haryana | 16 | 14 | 11 | 0 |
| 5 | 6 | Himachal Pradesh | 4 | 0 | 0 | 1 |
| 6 | 7 | Karnataka | 20 | 0 | 3 | 2 |
| 7 | 8 | Kerala | 131 | 7 | 11 | 0 |
| 8 | 9 | Madhya Pradesh | 23 | 0 | 0 | 1 |
| 9 | 10 | Maharashtra | 144 | 3 | 15 | 4 |
| 10 | 11 | Odisha | 3 | 0 | 0 | 0 |
| 11 | 12 | Puducherry | 1 | 0 | 0 | 0 |
| 12 | 13 | Punjab | 29 | 0 | 0 | 1 |
| 13 | 14 | Rajasthan | 41 | 2 | 3 | 0 |
| 14 | 15 | Tamil Nadu | 32 | 3 | 1 | 1 |
| 15 | 16 | Telengana | 34 | 11 | 1 | 0 |
| 16 | 17 | Chandigarh | 7 | 0 | 0 | 0 |
| 17 | 18 | Jammu and Kashmir | 18 | 0 | 1 | 1 |
| 18 | 19 | Ladakh | 13 | 0 | 0 | 0 |
| 19 | 20 | Uttar Pradesh | 42 | 1 | 11 | 0 |
| 20 | 21 | Uttarakhand | 4 | 0 | 0 | 0 |
| 21 | 22 | West Bengal | 11 | 0 | 0 | 1 |
| 22 | 23 | Bihar | 7 | 0 | 0 | 1 |
| 23 | 24 | Mizoram | 1 | 0 | 0 | 0 |
| 24 | 25 | Goa | 6 | 0 | 0 | 0 |
| 25 | 26 | Manipur | 1 | 0 | 0 | 0 |
df['Total Cases']=df['Total Confirmed cases (Indian National)']+df['Total Confirmed cases ( Foreign National )']
data
| districtData/0/district | blank | districtData/0/active | districtData/0/confirmed | districtData/0/deceased | districtData/0/recovered | |
|---|---|---|---|---|---|---|
| 0 | anthapur | NaN | 47 | 122 | 4 | 71 |
| 1 | chittor | NaN | 100 | 177 | 0 | 77 |
| 2 | eastgodavari | NaN | 13 | 52 | 0 | 39 |
| 3 | guntur | NaN | 134 | 417 | 8 | 275 |
| 4 | kadapa | NaN | 34 | 102 | 0 | 68 |
| 5 | krishna | NaN | 99 | 367 | 15 | 253 |
| 6 | kurnool | NaN | 187 | 611 | 19 | 405 |
| 7 | nellore | NaN | 53 | 150 | 3 | 94 |
| 8 | prakasam | NaN | 3 | 66 | 0 | 63 |
| 9 | srikakulam | NaN | 10 | 14 | 0 | 4 |
| 10 | vishakapatnam | NaN | 41 | 75 | 1 | 33 |
| 11 | vizayanagram | NaN | 7 | 7 | 0 | 0 |
| 12 | weatgodavari | NaN | 19 | 70 | 0 | 51 |
total_cases_overall=df['Total Cases'].sum()
print('The total number of cases till now in India is ',total_cases_overall)
The total number of cases till now in India is 729
df['Active Cases']=df['Total Cases']-(df['Death']+df['Cured'])
df
| S. No. | Name of State / UT | Total Confirmed cases (Indian National) | Total Confirmed cases ( Foreign National ) | Cured | Death | Total Cases | Active Cases | |
|---|---|---|---|---|---|---|---|---|
| 0 | 1 | Andhra Pradesh | 12 | 0 | 1 | 0 | 12 | 11 |
| 1 | 2 | Chhattisgarh | 6 | 0 | 0 | 0 | 6 | 6 |
| 2 | 3 | Delhi | 38 | 1 | 6 | 1 | 39 | 32 |
| 3 | 4 | Gujarat | 43 | 0 | 0 | 3 | 43 | 40 |
| 4 | 5 | Haryana | 16 | 14 | 11 | 0 | 30 | 19 |
| 5 | 6 | Himachal Pradesh | 4 | 0 | 0 | 1 | 4 | 3 |
| 6 | 7 | Karnataka | 20 | 0 | 3 | 2 | 20 | 15 |
| 7 | 8 | Kerala | 131 | 7 | 11 | 0 | 138 | 127 |
| 8 | 9 | Madhya Pradesh | 23 | 0 | 0 | 1 | 23 | 22 |
| 9 | 10 | Maharashtra | 144 | 3 | 15 | 4 | 147 | 128 |
| 10 | 11 | Odisha | 3 | 0 | 0 | 0 | 3 | 3 |
| 11 | 12 | Puducherry | 1 | 0 | 0 | 0 | 1 | 1 |
| 12 | 13 | Punjab | 29 | 0 | 0 | 1 | 29 | 28 |
| 13 | 14 | Rajasthan | 41 | 2 | 3 | 0 | 43 | 40 |
| 14 | 15 | Tamil Nadu | 32 | 3 | 1 | 1 | 35 | 33 |
| 15 | 16 | Telengana | 34 | 11 | 1 | 0 | 45 | 44 |
| 16 | 17 | Chandigarh | 7 | 0 | 0 | 0 | 7 | 7 |
| 17 | 18 | Jammu and Kashmir | 18 | 0 | 1 | 1 | 18 | 16 |
| 18 | 19 | Ladakh | 13 | 0 | 0 | 0 | 13 | 13 |
| 19 | 20 | Uttar Pradesh | 42 | 1 | 11 | 0 | 43 | 32 |
| 20 | 21 | Uttarakhand | 4 | 0 | 0 | 0 | 4 | 4 |
| 21 | 22 | West Bengal | 11 | 0 | 0 | 1 | 11 | 10 |
| 22 | 23 | Bihar | 7 | 0 | 0 | 1 | 7 | 6 |
| 23 | 24 | Mizoram | 1 | 0 | 0 | 0 | 1 | 1 |
| 24 | 25 | Goa | 6 | 0 | 0 | 0 | 6 | 6 |
| 25 | 26 | Manipur | 1 | 0 | 0 | 0 | 1 | 1 |
df.style.background_gradient(cmap='Reds')
| S. No. | Name of State / UT | Total Confirmed cases (Indian National) | Total Confirmed cases ( Foreign National ) | Cured | Death | Total Cases | Active Cases | |
|---|---|---|---|---|---|---|---|---|
| 0 | 1 | Andhra Pradesh | 12 | 0 | 1 | 0 | 12 | 11 |
| 1 | 2 | Chhattisgarh | 6 | 0 | 0 | 0 | 6 | 6 |
| 2 | 3 | Delhi | 38 | 1 | 6 | 1 | 39 | 32 |
| 3 | 4 | Gujarat | 43 | 0 | 0 | 3 | 43 | 40 |
| 4 | 5 | Haryana | 16 | 14 | 11 | 0 | 30 | 19 |
| 5 | 6 | Himachal Pradesh | 4 | 0 | 0 | 1 | 4 | 3 |
| 6 | 7 | Karnataka | 20 | 0 | 3 | 2 | 20 | 15 |
| 7 | 8 | Kerala | 131 | 7 | 11 | 0 | 138 | 127 |
| 8 | 9 | Madhya Pradesh | 23 | 0 | 0 | 1 | 23 | 22 |
| 9 | 10 | Maharashtra | 144 | 3 | 15 | 4 | 147 | 128 |
| 10 | 11 | Odisha | 3 | 0 | 0 | 0 | 3 | 3 |
| 11 | 12 | Puducherry | 1 | 0 | 0 | 0 | 1 | 1 |
| 12 | 13 | Punjab | 29 | 0 | 0 | 1 | 29 | 28 |
| 13 | 14 | Rajasthan | 41 | 2 | 3 | 0 | 43 | 40 |
| 14 | 15 | Tamil Nadu | 32 | 3 | 1 | 1 | 35 | 33 |
| 15 | 16 | Telengana | 34 | 11 | 1 | 0 | 45 | 44 |
| 16 | 17 | Chandigarh | 7 | 0 | 0 | 0 | 7 | 7 |
| 17 | 18 | Jammu and Kashmir | 18 | 0 | 1 | 1 | 18 | 16 |
| 18 | 19 | Ladakh | 13 | 0 | 0 | 0 | 13 | 13 |
| 19 | 20 | Uttar Pradesh | 42 | 1 | 11 | 0 | 43 | 32 |
| 20 | 21 | Uttarakhand | 4 | 0 | 0 | 0 | 4 | 4 |
| 21 | 22 | West Bengal | 11 | 0 | 0 | 1 | 11 | 10 |
| 22 | 23 | Bihar | 7 | 0 | 0 | 1 | 7 | 6 |
| 23 | 24 | Mizoram | 1 | 0 | 0 | 0 | 1 | 1 |
| 24 | 25 | Goa | 6 | 0 | 0 | 0 | 6 | 6 |
| 25 | 26 | Manipur | 1 | 0 | 0 | 0 | 1 | 1 |
Total_Active_Cases=df.groupby('Name of State / UT')['Total Cases'].sum().sort_values(ascending=False).to_frame()
Total_Active_Cases
| Total Cases | |
|---|---|
| Name of State / UT | |
| Maharashtra | 147 |
| Kerala | 138 |
| Telengana | 45 |
| Uttar Pradesh | 43 |
| Gujarat | 43 |
| Rajasthan | 43 |
| Delhi | 39 |
| Tamil Nadu | 35 |
| Haryana | 30 |
| Punjab | 29 |
| Madhya Pradesh | 23 |
| Karnataka | 20 |
| Jammu and Kashmir | 18 |
| Ladakh | 13 |
| Andhra Pradesh | 12 |
| West Bengal | 11 |
| Bihar | 7 |
| Chandigarh | 7 |
| Goa | 6 |
| Chhattisgarh | 6 |
| Himachal Pradesh | 4 |
| Uttarakhand | 4 |
| Odisha | 3 |
| Manipur | 1 |
| Mizoram | 1 |
| Puducherry | 1 |
Total_Active_Cases.style.background_gradient(cmap='Reds')
| Total Cases | |
|---|---|
| Name of State / UT | |
| Maharashtra | 147 |
| Kerala | 138 |
| Telengana | 45 |
| Uttar Pradesh | 43 |
| Gujarat | 43 |
| Rajasthan | 43 |
| Delhi | 39 |
| Tamil Nadu | 35 |
| Haryana | 30 |
| Punjab | 29 |
| Madhya Pradesh | 23 |
| Karnataka | 20 |
| Jammu and Kashmir | 18 |
| Ladakh | 13 |
| Andhra Pradesh | 12 |
| West Bengal | 11 |
| Bihar | 7 |
| Chandigarh | 7 |
| Goa | 6 |
| Chhattisgarh | 6 |
| Himachal Pradesh | 4 |
| Uttarakhand | 4 |
| Odisha | 3 |
| Manipur | 1 |
| Mizoram | 1 |
| Puducherry | 1 |
fig=plt.figure(figsize=(20,10),dpi=200)
axes=fig.add_axes([0,0,1,1])
axes.bar(df['Name of State / UT'],df['Total Cases'])
axes.set_title("Total Cases in India")
axes.set_xlabel("Name of State / UT")
axes.set_ylabel("Total Cases")
plt.show()
#plotly
fig=go.Figure()
fig.add_trace(go.Bar(x=df['Name of State / UT'],y=df['Total Cases']))
fig.update_layout(title='Total Cases in India',xaxis=dict(title='Name of State / UT'),yaxis=dict(title='Total Cases'))
data= pd.read_csv('Covid cases in India (3).csv') #st
data
| S. No. | Name of State / UT | Total Confirmed cases (Indian National) | Total Confirmed cases ( Foreign National ) | Cured | Death | |
|---|---|---|---|---|---|---|
| 0 | 1 | Andhra Pradesh | 12 | 0 | 1 | 0 |
| 1 | 2 | Chhattisgarh | 6 | 0 | 0 | 0 |
| 2 | 3 | Delhi | 38 | 1 | 6 | 1 |
| 3 | 4 | Gujarat | 43 | 0 | 0 | 3 |
| 4 | 5 | Haryana | 16 | 14 | 11 | 0 |
| 5 | 6 | Himachal Pradesh | 4 | 0 | 0 | 1 |
| 6 | 7 | Karnataka | 20 | 0 | 3 | 2 |
| 7 | 8 | Kerala | 131 | 7 | 11 | 0 |
| 8 | 9 | Madhya Pradesh | 23 | 0 | 0 | 1 |
| 9 | 10 | Maharashtra | 144 | 3 | 15 | 4 |
| 10 | 11 | Odisha | 3 | 0 | 0 | 0 |
| 11 | 12 | Puducherry | 1 | 0 | 0 | 0 |
| 12 | 13 | Punjab | 29 | 0 | 0 | 1 |
| 13 | 14 | Rajasthan | 41 | 2 | 3 | 0 |
| 14 | 15 | Tamil Nadu | 32 | 3 | 1 | 1 |
| 15 | 16 | Telengana | 34 | 11 | 1 | 0 |
| 16 | 17 | Chandigarh | 7 | 0 | 0 | 0 |
| 17 | 18 | Jammu and Kashmir | 18 | 0 | 1 | 1 |
| 18 | 19 | Ladakh | 13 | 0 | 0 | 0 |
| 19 | 20 | Uttar Pradesh | 42 | 1 | 11 | 0 |
| 20 | 21 | Uttarakhand | 4 | 0 | 0 | 0 |
| 21 | 22 | West Bengal | 11 | 0 | 0 | 1 |
| 22 | 23 | Bihar | 7 | 0 | 0 | 1 |
| 23 | 24 | Mizoram | 1 | 0 | 0 | 0 |
| 24 | 25 | Goa | 6 | 0 | 0 | 0 |
| 25 | 26 | Manipur | 1 | 0 | 0 | 0 |
Indian_Cord=pd.read_csv('Indian Coordinates.csv')
Indian_Cord
| Name of State / UT | Latitude | Longitude | Unnamed: 3 | |
|---|---|---|---|---|
| 0 | Andaman And Nicobar | 11.667026 | 92.735983 | NaN |
| 1 | Andhra Pradesh | 14.750429 | 78.570026 | NaN |
| 2 | Arunachal Pradesh | 27.100399 | 93.616601 | NaN |
| 3 | Assam | 26.749981 | 94.216667 | NaN |
| 4 | Bihar | 25.785414 | 87.479973 | NaN |
| 5 | Chandigarh | 30.719997 | 76.780006 | NaN |
| 6 | Chhattisgarh | 22.090420 | 82.159987 | NaN |
| 7 | Dadra And Nagar Haveli | 20.266578 | 73.016618 | NaN |
| 8 | Delhi | 28.669993 | 77.230004 | NaN |
| 9 | Goa | 15.491997 | 73.818001 | NaN |
| 10 | Haryana | 28.450006 | 77.019991 | NaN |
| 11 | Himachal Pradesh | 31.100025 | 77.166597 | NaN |
| 12 | Union Territory of Jammu and Kashmir | 33.450000 | 76.240000 | NaN |
| 13 | Jharkhand | 23.800393 | 86.419986 | NaN |
| 14 | Karnataka | 12.570381 | 76.919997 | NaN |
| 15 | Kerala | 8.900373 | 76.569993 | NaN |
| 16 | Lakshadweep | 10.562573 | 72.636867 | NaN |
| 17 | Madhya Pradesh | 21.300391 | 76.130019 | NaN |
| 18 | Maharashtra | 19.250232 | 73.160175 | NaN |
| 19 | Manipur | 24.799971 | 93.950017 | NaN |
| 20 | Meghalaya | 25.570492 | 91.880014 | NaN |
| 21 | Mizoram | 23.710399 | 92.720015 | NaN |
| 22 | Nagaland | 25.666998 | 94.116570 | NaN |
| 23 | Orissa | 19.820430 | 85.900017 | NaN |
| 24 | Puducherry | 11.934994 | 79.830000 | NaN |
| 25 | Punjab | 31.519974 | 75.980003 | NaN |
| 26 | Rajasthan | 26.449999 | 74.639981 | NaN |
| 27 | Sikkim | 27.333330 | 88.616647 | NaN |
| 28 | Telengana | 18.112400 | 79.019300 | NaN |
| 29 | Tamil Nadu | 12.920386 | 79.150042 | NaN |
| 30 | Tripura | 23.835404 | 91.279999 | NaN |
| 31 | Uttar Pradesh | 27.599981 | 78.050006 | NaN |
| 32 | Uttarakhand | 30.320409 | 78.050006 | NaN |
| 33 | West Bengal | 22.580390 | 88.329947 | NaN |
| 34 | Union Territory of Ladakh | 34.100000 | 77.340000 | NaN |
dd=pd.merge(Indian_Cord,df,on='Name of State / UT')
dd
| Name of State / UT | Latitude | Longitude | Unnamed: 3 | S. No. | Total Confirmed cases (Indian National) | Total Confirmed cases ( Foreign National ) | Cured | Death | Total Cases | Active Cases | |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | Andhra Pradesh | 14.750429 | 78.570026 | NaN | 1 | 12 | 0 | 1 | 0 | 12 | 11 |
| 1 | Delhi | 28.669993 | 77.230004 | NaN | 3 | 38 | 1 | 6 | 1 | 39 | 32 |
| 2 | Haryana | 28.450006 | 77.019991 | NaN | 5 | 16 | 14 | 11 | 0 | 30 | 19 |
| 3 | Karnataka | 12.570381 | 76.919997 | NaN | 7 | 20 | 0 | 3 | 2 | 20 | 15 |
| 4 | Kerala | 8.900373 | 76.569993 | NaN | 8 | 131 | 7 | 11 | 0 | 138 | 127 |
| 5 | Maharashtra | 19.250232 | 73.160175 | NaN | 10 | 144 | 3 | 15 | 4 | 147 | 128 |
| 6 | Punjab | 31.519974 | 75.980003 | NaN | 13 | 29 | 0 | 0 | 1 | 29 | 28 |
| 7 | Rajasthan | 26.449999 | 74.639981 | NaN | 14 | 41 | 2 | 3 | 0 | 43 | 40 |
| 8 | Telengana | 18.112400 | 79.019300 | NaN | 16 | 34 | 11 | 1 | 0 | 45 | 44 |
| 9 | Tamil Nadu | 12.920386 | 79.150042 | NaN | 15 | 32 | 3 | 1 | 1 | 35 | 33 |
| 10 | Uttar Pradesh | 27.599981 | 78.050006 | NaN | 20 | 42 | 1 | 11 | 0 | 43 | 32 |
| 11 | Uttarakhand | 30.320409 | 78.050006 | NaN | 21 | 4 | 0 | 0 | 0 | 4 | 4 |
map=folium.Map(location=[20,70],zoom_start=4,tiles='Stamenterrain')
for lat,long,value, name in zip(dd['Latitude'],dd['Longitude'],dd['Total Cases'],dd['Name of State / UT']):
folium.CircleMarker([lat,long],radius=value*0.8,popup=('<strong>State</strong>: '+str(name).capitalize()+'<br>''<strong>Total Cases</strong>: ' + str(value)+ '<br>'),color='red',fill_color='red',fill_opacity=0.2).add_to(map)
map